IOT and UAV Integrated System for Proactive Crop Disease Prediction
Disease prediction; Internet of things; Machine learning; Unmanned aerial vehicles
The productivity of agriculture is often affected by the crop diseases incurring economic loss and reduced food production. Detecting these crop diseases in the early stages is the need of the hour to mitigate the spread of the disease and manage the crop yield effectively. This paper presents a proactive crop disease prediction system by employing the technologies such as Unmanned Aerial Vehicles (UAVs) and Internet of Things (IoT) to detect and mitigate the crop disease at early stages. The employed UAVs have a high resolution camera to capture the aerial images of crops and leaves. The images captured via UAVs are transferred to a computing environment dynamically for analysis purposes. The analysis is performed on the images to predict the disease of the leaf along with its intensity. After analyzing and predicting the leaf disease and its intensity, the pesticide information is passed to the IoT sensors which are fixed in the fields to spray the recommended pesticide. The system analyzes the captured images by leveraging the machine learning algorithms in the realtime to identify different diseases and predict the spread of the disease. The proposed proactive approach helps the farmers to take preventive actions to identify the crop disease at the early stage and mitigate them in a timely manner to produce a better crop yield. Moreover, the proposed method provides a cost-effective solution for disease prediction and is easily accessible to farmers to enhance crop productivity. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
Sowjanya, K. Krishna; Madavi, K. P. Bindu
Studies in Computational Intelligence;Volume;1236;pp.287-303
Springer Science and Business Media Deutschland GmbH
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1007/978-3-032-05603-0_13 https://www.scopus.com/pages/publications/105024559425?origin=resultslist
Sowjanya K.K., CMR Institute of Technology, Bengaluru, India; Madavi K.P.B., Christ University, Bengaluru, India